Exact Algorithms for $L^1$-TV Regularization of Real-Valued or Circle-Valued Signals

DOI10.1137/15M101796XzbMath1382.94029arXiv1504.00499MaRDI QIDQ2790079

Andreas Weinmann, Martin Storath, Michael Unser

Publication date: 2 March 2016

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1504.00499

zbMATH Keywords

dynamic programmingtotal variation regularizationleast absolute deviationsdistance transformcircle-valued datatotal cyclic variation

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26) Numerical optimization and variational techniques (65K10) Dynamic programming (90C39) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (9)

Mumford-Shah and Potts regularization for manifold-valued data ⋮ A unified approach for a 1D generalized total variation problem ⋮ Wavelet Sparse Regularization for Manifold-Valued Data ⋮ A Faster Algorithm Solving a Generalization of Isotonic Median Regression and a Class of Fused Lasso Problems ⋮ Jump-penalized least absolute values estimation of scalar or circle-valued signals ⋮ Non-smooth Variational Regularization for Processing Manifold-Valued Data ⋮ Mini-workshop: Computational optimization on manifolds. Abstracts from the mini-workshop held November 15--21, 2020 (online meeting) ⋮ Total Generalized Variation for Manifold-Valued Data ⋮ An Inexact Semismooth Newton Method on Riemannian Manifolds with Application to Duality-Based Total Variation Denoising

Uses Software

• RecPF
• condat_tv

Cites Work

• Nonlinear total variation based noise removal algorithms
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